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Equipped with base station hardware, unmanned aerial vehicles (UAVs) can act as drone small cells (DSCs) to complement the traditional terrestrial small cells (TSCs) in providing ubiquitous connectivity for a wide range of applications. Although the performance of drone-assisted heterogeneous networks has been investigated, the state-of-the-art adopts simplified channel models that are not applicable to practical network scenarios. To make an accurate analysis on the network performance, and provide guidelines for the practical design of cell association and network deployment strategy, in this work, we consider a drone-assisted heterogeneous network that employs a flexible biased cell association policy and adopts a more general channel model that incorporates line-of-sight (LoS) and non-line-of-sight (NLoS) components for both air-to-ground (ATG) and ground-to-ground (GTG) transmissions. We develop a unifying analytical framework to evaluate the network performance in terms of coverage probability, area spectral efficiency (ASE) and user throughput. Our results show that 1) deploying DSCs to complement traditional TSCs can only deteriorate the network performance within the ultra-dense network region, 2) by optimally setting the bias factor for cell association policy, the network performance can be further enhanced. [ABSTRACT FROM AUTHOR]

Motivated by the advancing computational capacity of wireless end-user equipment (UE), as well as the increasing concerns about sharing private data, a new machine learning (ML) paradigm has emerged, namely federated learning (FL). Specifically, FL allows a decoupling of data provision at UEs and ML model aggregation at a central unit. By training model locally, FL is capable of avoiding direct data leakage from the UEs, thereby preserving privacy and security to some extent. However, even if raw data are not disclosed from UEs, an individual's private information can still be extracted by some recently discovered attacks against the FL architecture. In this work, we analyze the privacy and security issues in FL, and discuss several challenges to preserving privacy and security when designing FL systems. In addition, we provide extensive simulation results to showcase the discussed issues and possible solutions. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the multiple input multiple output (MIMO)-non-orthogonal multiple access (NOMA) transmission with the aid of location information. We first consider two users separated in both the distance and angle domains. With different access distances, NOMA could be used to serve the near-user and the far-user simultaneously, whereas spatial division multiple access (SDMA) would be applied if the two users have largely-separated angles of departure (AOD) that guarantees spatial orthogonality. Comparing the ergodic sum rates of these two multiple access (MA) schemes, we first characterize the preferable MA regions in the angle-distance plane. Analytical expression of the region boundary between NOMA and SDMA is derived. Moreover, NOMA-preferable regions are expressed in terms of the maximum distance difference and the minimum angle difference between the two users, respectively. On basis of these results, we further propose a location-based low-complexity user pairing algorithm for the general multiuser scenario. Numerical results confirm the accuracy of the derived region boundaries, and the simulations show that the proposed user pairing algorithm can effectively improve the resource utilization rate, compared to the conventional MA and user pairing schemes. [ABSTRACT FROM AUTHOR]

By computation offloading, fog radio access network (RAN) can support delay-constrained applications, since such a hierarchical network architecture enables parallel computing at local computing layer (LCL), edge computing layer (ECL), and cloud computing layer (CCL). Comparing to processing applications exclusively at the LCL, the assistance of processing at the ECL and the CCL can reduce computation latency, while introduce extra communication latency. These two latency are not only influenced by the hierarchical offloading decision but also by the available communication and computational resources at multiple computing layers. In this regard, this paper aims to jointly provision communication and computational resources for a powerful hierarchical offloading mechanism in fog RAN to complete delay-constrained applications with high-efficiency resource utilization. Specifically, we formulate a deadline-constrained energy minimization problem, which is followed by feasibility analysis, effective solution, and application extension. Through theoretical analyses and simulation results, we provide some insights on hierarchical offloading conditions and demonstrate advantages of our proposed algorithms in terms of the minimum affordable latency, total energy consumption, and scalability. [ABSTRACT FROM AUTHOR]

This work exploits the advantages of two prominent techniques in future communication networks, namely caching and non-orthogonal multiple access (NOMA). Particularly, a system with Rayleigh fading channels and cache-enabled users is analyzed. It is shown that the caching-NOMA combination provides a new opportunity of cache hit which enhances the cache utility as well as the effectiveness of NOMA. Importantly, this comes without requiring users’ collaboration, and thus, avoids many complicated issues such as users’ privacy and security, selfishness, etc. In order to optimize users’ quality of service and, concurrently, ensure the fairness among users, the probability that all users can decode the desired signals is maximized. In NOMA, a combination of multiple messages are sent to users, and the defined objective is approached by finding an appropriate power allocation for message signals. To address the power allocation problem, two novel methods are proposed. The first one is a divide-and-conquer-based method for which closed-form expressions for the optimal resource allocation policy are derived making this method simple and flexible to the system context. The second one is based on deep reinforcement learning method that allows all users to share the full bandwidth. Finally, simulation results are provided to demonstrate the effectiveness of the proposed methods and to compare their performance. [ABSTRACT FROM AUTHOR]

Long-term evolution (LTE) and wireless local area network (WLAN) are often presented as opposing technologies. Hence, efficient partitioning of the spectrum resources carries critical importance for achieving the coexistence of these on the unlicensed spectrum band. In this paper, we firstly develop an online spectrum partitioning algorithm, which needs little signal transmission and exchange between coordination manager and networks. Then, we focus on the convergence analysis of the online spectrum partitioning algorithm, which is difficult due to the time-varying wireless channels. To overcome this challenge, we model the algorithm and network dynamics as the stochastic differential equations (SDE) and show that the algorithm convergence is equivalent to the stochastic stability of a virtual stochastic dynamic system constructed by the SDEs. Then, we give the sufficient condition about the algorithm convergence and the upper bound on the tracking error of the spectrum partitioning algorithm under exogenous variations of time-varying channel state information (CSI). Based on the insights of the impact of time-varying CSI on algorithm convergence, an online compensative spectrum partitioning algorithm is developed to offset the tracking error caused by the disturbance of time-varying CSI. Through performance evaluation, we show that the coexistence performance efficiency will come at low expense of algorithm complexity and signal overhead. [ABSTRACT FROM AUTHOR]

Based on a hierarchical cloud-fog computing-enabled paradigm, fog radio access networks (F-RANs) can provide abundant resource to support the future mobile artificial intelligent services. However, due to the differences of computation and communication capabilities at the cloud computing center, the fog computing based access points (F-APs), and the user devices, it is challenging to propose efficient computation offloading strategies to fully explore the potential of F-RANs. In this paper, we study the design of computation offloading in F-RANs to minimize the total cost with respect to the energy consumption and the offloading latency. In particular, a joint optimization problem is formulated to optimize the offloading decision, the computation and the radio resources allocation. To solve this non-linear and non-convex problem, an iterative algorithm is designed, which can be proved to converge a stationary optimal solution with polynomial computational complexity. Finally, the simulation results are provided to show the performance gains of our proposed joint optimization algorithm. [ABSTRACT FROM AUTHOR]

In Internet of Things, wireless access networks are required to support a large number of user equipments (UEs) in real time. With UEs’ frequently arrival and departure, plenty of packet collisions can occur. Successive decoding thus becomes a promising technique to support the massive connectivity as it is capable of recovering packets from mixed received signals. In this paper, we propose an adaptive framing with successive decoding (AFSD) frame structure to deal with the fluidity of UEs. In the AFSD structure, the frame length is adaptively adjusted based on the present UE number, and successive decoding is adopted to alleviate the packet loss caused by packet collision. To quantify its performance, we provide exact as well as asymptotic results for the average delay. The analytical results are validated by simulations, and further extended to practical systems with estimated UE number, which shows that knowledge of active UE number is useful for delay reduction. Optimizations on UE transmission probability and frame length are also presented. In particular, the numerical studies reveal that by using optimized parameters, the average delay is reduced significantly for a wide range of arrival rates, which validates the effectiveness of the AFSD structure. [ABSTRACT FROM AUTHOR]

Network slicing is a fundamental architectural technology for the fifth generation mobile network. It is challenging to design a robust end-to-end network slice spanning overall networks, where a slice is constituted by a set of virtual network functions (VNFs) and links. Bugs may accidentally occur in some VNFs, invalidating some slices, and triggering slice recovery processes. Besides, the traffic demands in each slice can be stochastic, and drastic changes of traffic demands may trigger slice reconfiguration. In this paper, we investigate robust network slicing mechanisms by addressing the slice recovery and reconfiguration in a unified framework. We first develop an optimal slice recovery mechanism for deterministic traffic demands. This optimal solution is used as a benchmark for evaluating other robust slicing algorithms. Then, we design an optimal joint slice recovery and reconfiguration algorithm for stochastic traffic demands by exploiting robust optimization. To tackle the slow convergence issue in the robust optimization algorithm, we propose a heuristic algorithm based on variable neighborhood search. Numerical results reveal that our proposed robust network slicing algorithms can provide adjustable tolerance of traffic uncertainties compared with the deterministic algorithm. [ABSTRACT FROM AUTHOR]

Ultra-reliable and low-latency communications (URLLC) have stringent requirements on quality-of-service and network availability. Due to path loss and shadowing, it is very challenging to guarantee the stringent requirements of URLLC with satisfactory communication range. In this paper, we first provide a quantitative definition of network availability in the short blocklength regime: the probability that the reliability and latency requirements can be satisfied when the blocklength of channel codes is short. Then, we establish a framework to maximize the available range, defined as the maximal communication distance subject to the network availability requirement, by exploiting multi-connectivity. The basic idea is using both device-to-device (D2D) and cellular links to transmit each packet. The practical setup with correlated shadowing between D2D and cellular links is considered. Besides, since processing delay for decoding packets cannot be ignored in URLLC, its impacts on the available range are studied. By comparing the available ranges of different transmission modes, we obtained some useful insights on how to choose transmission modes. Simulation and numerical results validate our analysis and show that multi-connectivity can improve the available ranges of D2D and cellular links remarkably. [ABSTRACT FROM AUTHOR]

Different from a long-term evolution (LTE) system that monopolizes a licensed channel, the standalone LTE system in an unlicensed spectrum (SA LTE-U) is not allowed to occupy the unlicensed channel uninterruptedly, the starting point of its channel occupancy cannot be determined in advance, and its maximum channel occupancy time is restricted. Therefore, user equipments (UEs) may be backlogged during the channel unoccupancy time in their random access (RA) procedure, which causes severe collision during the channel occupancy time and unnecessary retransmissions of preambles in an SA LTE-U system. To address the aforesaid issues, we propose a user grouping and effective window size based RA protocol. In this protocol, the UEs are divided into several groups, and at any time only one group is activated and allowed for its UEs to send RA attempt, which avoids the intergroup UEs’ collision. The backlogged UEs of the activated group are scattered over multiple RA slots for them to send RA attempt, which alleviates the intragroup UEs’ collision. In addition, to avoid the unnecessary retransmissions of preambles caused by the channel uncertainty, the transmission of RA attempt and the reception of RA response are performed on the effective channel that consists of the channel occupancy periods only. The expressions of success and dropping rates, cumulative distribution function of preamble transmission times, utilization of carrying RA slots, and RA delay are derived. An optimization problem is formulated to maximize the utilization of carrying RA slots to obtain the optimal number of carrying RA slots. Simulation results demonstrate that the proposed protocol outperforms the baseline protocol in SA LTE-U systems. [ABSTRACT FROM AUTHOR]

Supporting cooperative radio among remote radio heads (RRHs) and elastic cloud service in the baseband unit (BBU) pool, cloud radio access network (C-RAN) is perceived as a promising solution for the next mobile network. In C-RAN, cooperative radio can enhance power saving at RRHs, along with impact on computation effort and power saving in the BBU pool. In turn, power saving at RRHs, benefited from cooperative radio is restricted by the constrained computation capacity provisioned by processors in the BBU pool. Besides, limited fronthauls, which support baseband signal transfer between the BBU pool and RRHs, affect the cooperative radio design and power consumption at RRH as well. By jointly optimizing transmit beamforming among RRHs and processor sleeping in the BBU pool, we exploit such interplay between communication and computation for system power minimization in C-RAN with limited fronthaul and computation capacity. Specifically, we formulate this problem as a mixed-integer non-linear programming problem (MINLP), and then leverage the special structure of the MINLP to make a near optimal decision on the set of active processors and transmit beamforming vectors with high efficiency. Finally, extensive numerical results demonstrate that our proposed algorithms can enforce processor sleeping and reduce system power consumption significantly. [ABSTRACT FROM AUTHOR]

Capacity of energy harvesting communications with deterministic energy arrival and finite battery size is investigated. An abstraction of the physical layer is considered, where binary sequences are transmitted through a binary symmetric channel, and a cost function is associated with the transmission of each symbol. Upper and lower bounds on the channel capacity are derived for the general case by studying the normalized exponent of the cardinality of the set of feasible input sequences. Several upper bounds on the exponent are proposed by studying supersets of the feasible set. Lower bounds are derived by applying the binary entropy-power inequality and by using specific signaling schemes based on a save-and-transmit strategy. Numerical results are presented for several values of the energy arrival rate and battery size, validating the usefulness of the capacity bounds established for the energy harvesting channels. [ABSTRACT FROM PUBLISHER]

In this paper, we propose an optimization framework of offloading from a single mobile device (MD) to multiple edge devices. We aim to minimize both total tasks’ execution latency and the MD’s energy consumption by jointly optimizing the task allocation decision and the MD’s central process unit (CPU) frequency. This paper considers two cases for the MD, i.e., fixed CPU frequency and elastic CPU frequency. Since these problems are NP-hard, we propose a linear relaxation-based approach and a semidefinite relaxation (SDR)-based approach for the fixed CPU frequency case, and an exhaustive search-based approach and an SDR-based approach for the elastic CPU frequency case. Our simulation results show that the SDR-based algorithms achieve near optimal performance. Performance improvement can be obtained with the proposed scheme in terms of energy consumption and tasks’ execution latency when multiple edge devices and elastic CPU frequency are considered. Finally, we show that the MD’s flexible CPU range can have an impact on the task allocation. [ABSTRACT FROM PUBLISHER]

In this paper, we study the inter-linked problems of wireless service provider (WSP) selection of users and bandwidth allocation of WSPs in multi-tier heterogeneous cellular networks employing the approach combining stochastic geometry and game theory. In particular, the expected average user achievable rate is calculated by modeling the distributions of users and base stations (BSs) as independent homogeneous Poisson point processes. Moreover, a hierarchical game framework is presented to model the complicated interactions among users and WSPs. Wherein, the evolutionary game, non-cooperative game, and multi-leader multi-follower Stackelberg game models are, respectively, adopted to formulate the competition among users, competition among WSPs, and cyclic dependence between users and WSPs. According to backward induction, the formulated Stackelberg game would be solved after the formulated evolutionary game and non-cooperative game are sequentially studied. For the evolutionary game, both the closed-form expression and the asymptotically stability of its evolutionary equilibrium (EE) were analyzed. Then, conditioned on the obtained EE, the existence of Nash equilibrium (NE) for the non-cooperative bandwidth allocation game is established; furthermore, a sufficient condition for the uniqueness of the NE is derived. Finally, extensive simulation results verify both the validity of our analysis and the effectiveness of the proposed scheme. [ABSTRACT FROM AUTHOR]

In 5G heterogeneous networking, it is promising to integrate different wireless networks to provide higher data rate. This paper models the integrated system as a receiver frequency division relay channel (RFDRC) and studies how to improve the transmission rate by combining decode-forward (DF), compress-forward (CF), and amplify-forward (AF) schemes. First, we establish clear criterions on how to select a relay scheme among DF, CF, and AF schemes and prove that the CF outperforms AF for all possible configurations. Based on the scheme selection criterions, we propose a hybrid DF–CF scheme which takes advantage of both DF and CF schemes in RFDRC. A near-optimal resource allocation is presented for the DF–CF-based system, leading to a new achievable rate for RFDRC. For ease of implementation, we further put forward a hybrid DF–AF scheme and reconsider the joint bandwidth and power allocation. Two suboptimal resource allocation solutions are established. In particular, when source frequency band and relay frequency band have equivalent bandwidth, we show that the proposed hybrid DF–AF scheme can achieve the concave envelope of the maximum between DF rate and AF rate. Numerical results show that the proposed schemes bring significant gains for RFDRC. [ABSTRACT FROM AUTHOR]